Overview

Dataset statistics

Number of variables12
Number of observations567
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.3 KiB
Average record size in memory96.2 B

Variable types

Numeric12

Alerts

u_q is highly correlated with coolant and 7 other fieldsHigh correlation
coolant is highly correlated with u_q and 7 other fieldsHigh correlation
stator_winding is highly correlated with u_q and 8 other fieldsHigh correlation
u_d is highly correlated with u_q and 8 other fieldsHigh correlation
stator_tooth is highly correlated with u_q and 8 other fieldsHigh correlation
i_d is highly correlated with u_q and 7 other fieldsHigh correlation
i_q is highly correlated with coolant and 7 other fieldsHigh correlation
pm is highly correlated with u_q and 8 other fieldsHigh correlation
stator_yoke is highly correlated with u_q and 8 other fieldsHigh correlation
ambient is highly correlated with u_q and 8 other fieldsHigh correlation
u_q is highly correlated with u_d and 4 other fieldsHigh correlation
coolant is highly correlated with stator_winding and 3 other fieldsHigh correlation
stator_winding is highly correlated with coolant and 4 other fieldsHigh correlation
u_d is highly correlated with u_q and 4 other fieldsHigh correlation
stator_tooth is highly correlated with coolant and 4 other fieldsHigh correlation
motor_speed is highly correlated with u_q and 4 other fieldsHigh correlation
i_d is highly correlated with u_q and 4 other fieldsHigh correlation
i_q is highly correlated with u_q and 4 other fieldsHigh correlation
pm is highly correlated with coolant and 4 other fieldsHigh correlation
stator_yoke is highly correlated with coolant and 4 other fieldsHigh correlation
ambient is highly correlated with stator_winding and 3 other fieldsHigh correlation
torque is highly correlated with u_q and 4 other fieldsHigh correlation
u_q is highly correlated with stator_winding and 5 other fieldsHigh correlation
coolant is highly correlated with stator_winding and 4 other fieldsHigh correlation
stator_winding is highly correlated with u_q and 7 other fieldsHigh correlation
u_d is highly correlated with u_q and 7 other fieldsHigh correlation
stator_tooth is highly correlated with u_q and 7 other fieldsHigh correlation
i_d is highly correlated with u_q and 6 other fieldsHigh correlation
i_q is highly correlated with stator_winding and 5 other fieldsHigh correlation
pm is highly correlated with u_q and 7 other fieldsHigh correlation
stator_yoke is highly correlated with u_q and 7 other fieldsHigh correlation
u_q is highly correlated with u_d and 4 other fieldsHigh correlation
coolant is highly correlated with stator_winding and 4 other fieldsHigh correlation
stator_winding is highly correlated with coolant and 5 other fieldsHigh correlation
u_d is highly correlated with u_q and 4 other fieldsHigh correlation
stator_tooth is highly correlated with coolant and 4 other fieldsHigh correlation
motor_speed is highly correlated with u_q and 5 other fieldsHigh correlation
i_d is highly correlated with u_q and 4 other fieldsHigh correlation
i_q is highly correlated with u_q and 4 other fieldsHigh correlation
pm is highly correlated with coolant and 4 other fieldsHigh correlation
stator_yoke is highly correlated with coolant and 4 other fieldsHigh correlation
ambient is highly correlated with coolant and 4 other fieldsHigh correlation
torque is highly correlated with u_q and 4 other fieldsHigh correlation
u_q has unique values Unique
coolant has unique values Unique
stator_winding has unique values Unique
i_q has unique values Unique
pm has unique values Unique

Reproduction

Analysis started2022-05-08 04:08:58.880560
Analysis finished2022-05-08 04:10:55.724396
Duration1 minute and 56.84 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

u_q
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct567
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.11237614
Minimum-0.974432528
Maximum95.68653107
Zeros0
Zeros (%)0.0%
Negative25
Negative (%)4.4%
Memory size4.6 KiB
2022-05-08T09:40:59.991859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.974432528
5-th percentile52.35301933
Q190.13275528
median90.53261566
Q391.00325775
95-th percentile91.61947174
Maximum95.68653107
Range96.6609636
Interquartile range (IQR)0.87050247

Descriptive statistics

Standard deviation19.550879
Coefficient of variation (CV)0.2270391305
Kurtosis15.32141058
Mean86.11237614
Median Absolute Deviation (MAD)0.43515014
Skewness-4.134590458
Sum48825.71727
Variance382.2368697
MonotonicityNot monotonic
2022-05-08T09:41:00.415804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4506815081
 
0.2%
90.535308841
 
0.2%
90.031251
 
0.2%
90.142768861
 
0.2%
90.281616211
 
0.2%
90.497024541
 
0.2%
90.542724611
 
0.2%
90.583412171
 
0.2%
90.546760561
 
0.2%
90.010047911
 
0.2%
Other values (557)557
98.2%
ValueCountFrequency (%)
-0.9744325281
0.2%
-0.9611871241
0.2%
-0.9455317851
0.2%
-0.9450922611
0.2%
-0.9164708851
0.2%
-0.9128165841
0.2%
-0.8912748691
0.2%
-0.8860744241
0.2%
-0.8743074541
0.2%
-0.8646435741
0.2%
ValueCountFrequency (%)
95.686531071
0.2%
94.830261231
0.2%
94.44209291
0.2%
93.565223691
0.2%
92.987953191
0.2%
92.546913151
0.2%
92.403854371
0.2%
92.205909731
0.2%
92.096733091
0.2%
92.011482241
0.2%

coolant
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct567
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.07997315
Minimum18.77373123
Maximum19.38357925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-05-08T09:41:00.855749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.77373123
5-th percentile18.80835609
Q118.94029999
median19.08385086
Q319.23428345
95-th percentile19.34343853
Maximum19.38357925
Range0.60984802
Interquartile range (IQR)0.29398346

Descriptive statistics

Standard deviation0.1723424669
Coefficient of variation (CV)0.009032636765
Kurtosis-1.183930149
Mean19.07997315
Median Absolute Deviation (MAD)0.14613342
Skewness-0.01441750769
Sum10818.34477
Variance0.0297019259
MonotonicityNot monotonic
2022-05-08T09:41:01.287687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.805171971
 
0.2%
19.091524121
 
0.2%
19.11370851
 
0.2%
19.086963651
 
0.2%
19.055368421
 
0.2%
19.038188931
 
0.2%
19.053058621
 
0.2%
19.062898641
 
0.2%
19.148275381
 
0.2%
19.236007691
 
0.2%
Other values (557)557
98.2%
ValueCountFrequency (%)
18.773731231
0.2%
18.77593041
0.2%
18.778673171
0.2%
18.778882981
0.2%
18.779071811
0.2%
18.779853821
0.2%
18.780735021
0.2%
18.781654361
0.2%
18.782293321
0.2%
18.784399031
0.2%
ValueCountFrequency (%)
19.383579251
0.2%
19.377178191
0.2%
19.373008731
0.2%
19.372413641
0.2%
19.37199021
0.2%
19.365587231
0.2%
19.364587781
0.2%
19.362415311
0.2%
19.359954831
0.2%
19.358854291
0.2%

stator_winding
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct567
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.68264909
Minimum19.0493412
Maximum58.08734512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-05-08T09:41:01.759622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19.0493412
5-th percentile19.09492168
Q132.18675232
median44.45065689
Q352.67630577
95-th percentile57.3220356
Maximum58.08734512
Range39.03800392
Interquartile range (IQR)20.48955345

Descriptive statistics

Standard deviation12.38811493
Coefficient of variation (CV)0.2972007586
Kurtosis-1.030634885
Mean41.68264909
Median Absolute Deviation (MAD)9.20370483
Skewness-0.4821765502
Sum23634.06203
Variance153.4653915
MonotonicityNot monotonic
2022-05-08T09:41:02.207564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.086669921
 
0.2%
50.585811611
 
0.2%
49.990509031
 
0.2%
50.13439561
 
0.2%
50.321762081
 
0.2%
50.485630041
 
0.2%
50.526119231
 
0.2%
50.490089421
 
0.2%
50.62512971
 
0.2%
50.021545411
 
0.2%
Other values (557)557
98.2%
ValueCountFrequency (%)
19.04934121
0.2%
19.052419661
0.2%
19.053178791
0.2%
19.058530811
0.2%
19.063886641
0.2%
19.074583051
0.2%
19.076025011
0.2%
19.077108381
0.2%
19.077804571
0.2%
19.078054431
0.2%
ValueCountFrequency (%)
58.087345121
0.2%
58.078205111
0.2%
58.053974151
0.2%
58.048870091
0.2%
58.034530641
0.2%
58.029678341
0.2%
58.021953581
0.2%
57.958450321
0.2%
57.853969571
0.2%
57.708469391
0.2%

u_d
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct566
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-87.60478313
Minimum-94.13155365
Maximum0.851180732
Zeros0
Zeros (%)0.0%
Negative547
Negative (%)96.5%
Memory size4.6 KiB
2022-05-08T09:41:02.655501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-94.13155365
5-th percentile-93.73479767
Q1-93.2789917
median-92.77215576
Q3-92.19697189
95-th percentile-26.65298901
Maximum0.851180732
Range94.98273438
Interquartile range (IQR)1.08201981

Descriptive statistics

Standard deviation20.62741196
Coefficient of variation (CV)-0.2354598827
Kurtosis13.43662768
Mean-87.60478313
Median Absolute Deviation (MAD)0.53672028
Skewness3.885586345
Sum-49671.91204
Variance425.4901242
MonotonicityNot monotonic
2022-05-08T09:41:03.135449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-92.307746892
 
0.4%
-0.3500545921
 
0.2%
-93.368415831
 
0.2%
-93.113174441
 
0.2%
-93.046951291
 
0.2%
-92.868591311
 
0.2%
-92.876976011
 
0.2%
-92.880172731
 
0.2%
-92.850982671
 
0.2%
-92.877746581
 
0.2%
Other values (556)556
98.1%
ValueCountFrequency (%)
-94.131553651
0.2%
-94.118164061
0.2%
-94.083213811
0.2%
-94.081771851
0.2%
-94.052268981
0.2%
-93.99553681
0.2%
-93.983642581
0.2%
-93.938087461
0.2%
-93.931068421
0.2%
-93.920227051
0.2%
ValueCountFrequency (%)
0.8511807321
0.2%
0.8446675541
0.2%
0.8438896541
0.2%
0.8335536721
0.2%
0.8279225231
0.2%
0.8117251991
0.2%
0.8105266091
0.2%
0.8081907631
0.2%
0.803170861
0.2%
0.8010491131
0.2%

stator_tooth
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct559
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.11919072
Minimum18.27636528
Maximum45.23299789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-05-08T09:41:03.951329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.27636528
5-th percentile18.29252949
Q126.02239132
median34.63823318
Q339.50568008
95-th percentile44.48737488
Maximum45.23299789
Range26.95663261
Interquartile range (IQR)13.48328876

Descriptive statistics

Standard deviation8.660070175
Coefficient of variation (CV)0.261481938
Kurtosis-1.154421409
Mean33.11919072
Median Absolute Deviation (MAD)7.02804184
Skewness-0.3846112868
Sum18778.58114
Variance74.99681544
MonotonicityNot monotonic
2022-05-08T09:41:04.383273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.452285777
 
1.2%
39.452281952
 
0.4%
39.452278142
 
0.4%
18.293218611
 
0.2%
39.122867581
 
0.2%
38.767852781
 
0.2%
38.829990391
 
0.2%
38.912334441
 
0.2%
38.987213131
 
0.2%
39.030525211
 
0.2%
Other values (549)549
96.8%
ValueCountFrequency (%)
18.276365281
0.2%
18.277015691
0.2%
18.277559281
0.2%
18.280504231
0.2%
18.283918381
0.2%
18.28402711
0.2%
18.284276961
0.2%
18.284282681
0.2%
18.284530641
0.2%
18.284809111
0.2%
ValueCountFrequency (%)
45.232997891
0.2%
45.22814561
0.2%
45.216049191
0.2%
45.196544651
0.2%
45.179851531
0.2%
45.154232031
0.2%
45.138050081
0.2%
45.136154171
0.2%
45.101394651
0.2%
45.05048371
0.2%

motor_speed
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct116
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4715.473193
Minimum-0.004142461
Maximum4999.970215
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)1.4%
Memory size4.6 KiB
2022-05-08T09:41:04.799219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.004142461
5-th percentile1103.618579
Q14999.952393
median4999.955566
Q34999.958984
95-th percentile4999.962402
Maximum4999.970215
Range4999.974357
Interquartile range (IQR)0.0065915

Descriptive statistics

Standard deviation1114.399187
Coefficient of variation (CV)0.2363281777
Kurtosis13.08263129
Mean4715.473193
Median Absolute Deviation (MAD)0.003417999999
Skewness-3.843586071
Sum2673673.3
Variance1241885.548
MonotonicityNot monotonic
2022-05-08T09:41:05.231155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4999.95605532
 
5.6%
4999.95263726
 
4.6%
4999.95898426
 
4.6%
4999.95947325
 
4.4%
4999.9545924
 
4.2%
4999.95410223
 
4.1%
4999.95556622
 
3.9%
4999.95703122
 
3.9%
4999.95312521
 
3.7%
4999.95849619
 
3.4%
Other values (106)327
57.7%
ValueCountFrequency (%)
-0.0041424611
0.2%
-0.0035653251
0.2%
-0.0032630931
0.2%
-0.0015386211
0.2%
-0.0014518661
0.2%
-0.0012790841
0.2%
-0.001247881
0.2%
-0.000580391
0.2%
0.000162571
0.2%
0.0002567821
0.2%
ValueCountFrequency (%)
4999.9702151
 
0.2%
4999.968751
 
0.2%
4999.9677732
0.4%
4999.9672851
 
0.2%
4999.9663091
 
0.2%
4999.965822
0.4%
4999.9653323
0.5%
4999.9648441
 
0.2%
4999.9643552
0.4%
4999.9638673
0.5%

i_d
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct564
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-133.3414816
Minimum-143.6170044
Maximum0.004419137
Zeros0
Zeros (%)0.0%
Negative563
Negative (%)99.3%
Memory size4.6 KiB
2022-05-08T09:41:05.639104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-143.6170044
5-th percentile-142.9534225
Q1-142.078598
median-141.4842224
Q3-141.0211716
95-th percentile-11.10263701
Maximum0.004419137
Range143.6214235
Interquartile range (IQR)1.0574264

Descriptive statistics

Standard deviation32.22045855
Coefficient of variation (CV)-0.2416386722
Kurtosis12.18816915
Mean-133.3414816
Median Absolute Deviation (MAD)0.509613
Skewness3.736985201
Sum-75604.62004
Variance1038.157949
MonotonicityNot monotonic
2022-05-08T09:41:06.103040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-140.94029242
 
0.4%
-141.15165712
 
0.4%
-141.26519782
 
0.4%
-141.5334931
 
0.2%
-141.04307561
 
0.2%
-141.17190551
 
0.2%
-141.33827211
 
0.2%
-141.44467161
 
0.2%
-141.54582211
 
0.2%
-141.55767821
 
0.2%
Other values (554)554
97.7%
ValueCountFrequency (%)
-143.61700441
0.2%
-143.6136781
0.2%
-143.597581
0.2%
-143.56970211
0.2%
-143.51382451
0.2%
-143.49758911
0.2%
-143.47497561
0.2%
-143.46101381
0.2%
-143.35740661
0.2%
-143.32260131
0.2%
ValueCountFrequency (%)
0.0044191371
0.2%
0.0012895871
0.2%
0.0006058721
0.2%
2.56 × 10-51
0.2%
-0.0643167791
0.2%
-0.6136352421
0.2%
-1.0056473021
0.2%
-1.2883837221
0.2%
-1.4905304911
0.2%
-1.6344635491
0.2%

i_q
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct567
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.53147466
Minimum-0.000785353
Maximum55.30832672
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.2%
Memory size4.6 KiB
2022-05-08T09:41:06.542982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.000785353
5-th percentile49.08450127
Q152.8778553
median52.96751404
Q353.04323578
95-th percentile53.11783371
Maximum55.30832672
Range55.30911207
Interquartile range (IQR)0.16538048

Descriptive statistics

Standard deviation10.90572895
Coefficient of variation (CV)0.2158205163
Kurtosis16.62651105
Mean50.53147466
Median Absolute Deviation (MAD)0.08128738
Skewness-4.293715872
Sum28651.34613
Variance118.934924
MonotonicityNot monotonic
2022-05-08T09:41:06.982922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0003281021
 
0.2%
52.956371311
 
0.2%
53.020431521
 
0.2%
52.985378271
 
0.2%
52.999549871
 
0.2%
52.979408261
 
0.2%
52.997779851
 
0.2%
52.984992981
 
0.2%
52.966960911
 
0.2%
52.998058321
 
0.2%
Other values (557)557
98.2%
ValueCountFrequency (%)
-0.0007853531
0.2%
0.0003281021
0.2%
0.0003864681
0.2%
0.0020456611
0.2%
0.0371837761
0.2%
0.3367473481
0.2%
0.5542112591
0.2%
0.7063699961
0.2%
0.817339481
0.2%
0.8980128771
0.2%
ValueCountFrequency (%)
55.308326721
0.2%
55.179786681
0.2%
54.71633531
0.2%
54.394992831
0.2%
54.131145481
0.2%
54.018875121
0.2%
53.784885411
0.2%
53.489444731
0.2%
53.298057561
0.2%
53.181751251
0.2%

pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct567
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.14934821
Minimum24.26473618
Maximum41.90695572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-05-08T09:41:07.398865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum24.26473618
5-th percentile24.5269825
Q128.4506197
median33.49811554
Q337.93897629
95-th percentile41.16778984
Maximum41.90695572
Range17.64221954
Interquartile range (IQR)9.488356595

Descriptive statistics

Standard deviation5.425537284
Coefficient of variation (CV)0.1636695011
Kurtosis-1.248808433
Mean33.14934821
Median Absolute Deviation (MAD)4.68863678
Skewness-0.1186308826
Sum18795.68044
Variance29.43645482
MonotonicityNot monotonic
2022-05-08T09:41:07.838807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.554214481
 
0.2%
36.593547821
 
0.2%
36.330451971
 
0.2%
36.441761021
 
0.2%
36.516609191
 
0.2%
36.481758121
 
0.2%
36.541095731
 
0.2%
36.604812621
 
0.2%
36.627815251
 
0.2%
36.345752721
 
0.2%
Other values (557)557
98.2%
ValueCountFrequency (%)
24.264736181
0.2%
24.285898211
0.2%
24.319919591
0.2%
24.351060871
0.2%
24.365470891
0.2%
24.372678761
0.2%
24.372970581
0.2%
24.388488771
0.2%
24.4050561
0.2%
24.43077661
0.2%
ValueCountFrequency (%)
41.906955721
0.2%
41.879280091
0.2%
41.85156251
0.2%
41.788505551
0.2%
41.720798491
0.2%
41.714351651
0.2%
41.700508121
0.2%
41.69025041
0.2%
41.657005311
0.2%
41.615016941
0.2%

stator_yoke
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct565
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.62475703
Minimum18.30085182
Maximum33.11236572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-05-08T09:41:08.270748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18.30085182
5-th percentile18.32334003
Q119.85061741
median26.16999817
Q330.19463444
95-th percentile32.57273369
Maximum33.11236572
Range14.8115139
Interquartile range (IQR)10.34401703

Descriptive statistics

Standard deviation4.967909207
Coefficient of variation (CV)0.1938714658
Kurtosis-1.40593015
Mean25.62475703
Median Absolute Deviation (MAD)4.77677918
Skewness-0.1011993412
Sum14529.23723
Variance24.68012189
MonotonicityNot monotonic
2022-05-08T09:41:08.662699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.850618362
 
0.4%
19.850616462
 
0.4%
29.016817091
 
0.2%
28.906158451
 
0.2%
28.909526821
 
0.2%
28.920270921
 
0.2%
28.934959411
 
0.2%
28.954467771
 
0.2%
28.972347261
 
0.2%
29.075319291
 
0.2%
Other values (555)555
97.9%
ValueCountFrequency (%)
18.300851821
0.2%
18.301168441
0.2%
18.301733021
0.2%
18.302207951
0.2%
18.303592681
0.2%
18.303977971
0.2%
18.305416111
0.2%
18.306240081
0.2%
18.306924821
0.2%
18.30798341
0.2%
ValueCountFrequency (%)
33.112365721
0.2%
33.109367371
0.2%
33.101325991
0.2%
33.088874821
0.2%
33.060062411
0.2%
33.025840761
0.2%
32.979797361
0.2%
32.959953311
0.2%
32.946468351
0.2%
32.944595341
0.2%

ambient
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct417
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.18835268
Minimum19.85062027
Maximum21.49830246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-05-08T09:41:09.126633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19.85062027
5-th percentile19.85062027
Q119.85063362
median19.87808418
Q320.41238403
95-th percentile21.38020801
Maximum21.49830246
Range1.64768219
Interquartile range (IQR)0.56175041

Descriptive statistics

Standard deviation0.5245543398
Coefficient of variation (CV)0.02598301843
Kurtosis0.2707138051
Mean20.18835268
Median Absolute Deviation (MAD)0.02746391
Skewness1.353833613
Sum11446.79597
Variance0.2751572554
MonotonicityNot monotonic
2022-05-08T09:41:09.534585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.85062027113
 
19.9%
19.8506221811
 
1.9%
19.850624086
 
1.1%
19.850629814
 
0.7%
19.850625994
 
0.7%
19.850633623
 
0.5%
19.851963043
 
0.5%
19.860559462
 
0.4%
19.865934372
 
0.4%
19.850637442
 
0.4%
Other values (407)417
73.5%
ValueCountFrequency (%)
19.85062027113
19.9%
19.8506221811
 
1.9%
19.850624086
 
1.1%
19.850625994
 
0.7%
19.85062792
 
0.4%
19.850629814
 
0.7%
19.850631711
 
0.2%
19.850633623
 
0.5%
19.850637442
 
0.4%
19.850639342
 
0.4%
ValueCountFrequency (%)
21.498302461
0.2%
21.493152621
0.2%
21.487039571
0.2%
21.486188891
0.2%
21.478216171
0.2%
21.477647781
0.2%
21.471235281
0.2%
21.468753811
0.2%
21.466455461
0.2%
21.459480291
0.2%

torque
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct565
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.79233709
Minimum0.176615342
Maximum48.66204834
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-05-08T09:41:09.990519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.176615342
5-th percentile23.30281887
Q148.34521484
median48.40164566
Q348.46291542
95-th percentile48.56690597
Maximum48.66204834
Range48.485433
Interquartile range (IQR)0.117700585

Descriptive statistics

Standard deviation10.34060589
Coefficient of variation (CV)0.225815203
Kurtosis13.9499052
Mean45.79233709
Median Absolute Deviation (MAD)0.05731201
Skewness-3.935018937
Sum25964.25513
Variance106.9281302
MonotonicityNot monotonic
2022-05-08T09:41:10.422472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.381557462
 
0.4%
48.324428562
 
0.4%
48.497707371
 
0.2%
48.583477021
 
0.2%
48.648246771
 
0.2%
48.57018281
 
0.2%
48.567863461
 
0.2%
48.564338681
 
0.2%
48.523429871
 
0.2%
0.1871007981
 
0.2%
Other values (555)555
97.9%
ValueCountFrequency (%)
0.1766153421
0.2%
0.1871007981
0.2%
0.2081966551
0.2%
0.2383027081
0.2%
0.2454174911
0.2%
0.4762178361
0.2%
0.6384467481
0.2%
0.6700153351
0.2%
0.7520354991
0.2%
0.9105414151
0.2%
ValueCountFrequency (%)
48.662048341
0.2%
48.651737211
0.2%
48.648246771
0.2%
48.641510011
0.2%
48.631969451
0.2%
48.625202181
0.2%
48.606868741
0.2%
48.601428991
0.2%
48.599288941
0.2%
48.5961381
0.2%

Interactions

2022-05-08T09:40:48.589351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:39:56.537612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:02.512505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:07.762843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:12.266236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:16.785624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:21.137047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:25.560448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:30.327807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:34.671221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:39.206616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:44.045966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:48.965300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:39:57.788429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:02.912447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:08.098792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:12.586188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:17.161577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:21.488996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:25.944398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:30.647761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:35.063172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:39.558568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:44.373917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:49.309255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:39:58.196666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:03.568358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:08.482740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:13.002134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:17.513529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:21.888941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:26.304359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:31.047710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:35.415121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:39.958510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:44.789862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:49.677207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:39:58.573368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:04.160279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:08.866694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:13.378085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:17.937470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:22.264892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:26.712290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:31.431662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:35.831068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:40.342458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:45.213806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:50.045159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:39:58.903659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:04.504233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:09.202646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:13.698041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:18.257430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:22.600848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:27.328210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:31.751614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:36.183022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:40.662421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:45.557762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:50.429108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:39:59.404613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:04.880187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:09.562595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:14.081989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:18.593385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:23.016790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:27.680163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:32.119564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:36.542973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:41.062366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:45.957706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:50.797058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:39:59.887252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:05.320123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:09.922550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:14.425943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:18.985329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:23.376738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:28.080110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:32.463522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:36.926919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:41.678281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:46.301658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:51.173008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:00.343967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:05.880049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:10.346495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:14.785895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:19.345282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:23.752690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:28.456061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:32.831473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:37.286869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:42.078226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:46.677607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:51.500960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:00.832105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:06.248002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:10.706445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:15.305828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:19.665237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:24.128643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:28.832021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:33.191421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:37.638827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:42.454174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:47.085554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:51.892907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:01.256064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:06.651002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:11.114391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:15.657780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:20.073185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:24.480591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:29.223962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:33.567373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:38.078764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:42.862125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:47.461503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:52.260857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:01.799994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:07.034948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:11.490339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:16.073725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:20.425154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:24.864544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:29.583907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:33.983318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:38.462719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:43.254071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:47.869449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:52.628806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:02.168237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:07.426883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:11.906280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:16.409676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:20.777094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:25.232493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:29.983855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:34.335273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:38.846662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:43.646017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-08T09:40:48.245399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-08T09:41:10.838403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-08T09:41:11.414325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-08T09:41:11.958255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-08T09:41:12.446188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-08T09:40:53.236729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-08T09:40:54.052621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

u_qcoolantstator_windingu_dstator_toothmotor_speedi_di_qpmstator_yokeambienttorque
0-0.45068218.80517219.086670-0.35005518.2932190.0028660.0044190.00032824.55421418.31654719.8506910.187101
1-0.32573718.81857119.092390-0.30580318.2948070.0002570.000606-0.00078524.53807818.31495519.8506720.245417
2-0.44086418.82877019.089380-0.37250318.2940940.0023550.0012900.00038624.54469318.32630719.8506570.176615
3-0.32702618.83556719.083031-0.31619918.2925410.0061050.0000260.00204624.55401818.33083319.8506470.238303
4-0.47115018.85703319.082525-0.33227218.2914280.003133-0.0643170.03718424.56539718.32666219.8506390.208197
5-0.53897318.90154819.0771080.00914718.2906280.009636-0.6136350.33674724.57360118.32386219.8506340.476218
6-0.65314818.94171119.0745830.23889018.2925240.001337-1.0056470.55421124.57657818.32193619.8506300.670015
7-0.75839218.96086119.0824990.39509918.2940410.001422-1.2883840.70637024.57494918.31465519.8506280.752035
8-0.72712818.97354519.0855330.54662318.2919640.000577-1.4905300.81733924.56708018.30692519.8506260.910541
9-0.87430718.98781219.0760250.57894418.287233-0.001248-1.6344640.89801324.55324218.30173319.8506240.924010

Last rows

u_qcoolantstator_windingu_dstator_toothmotor_speedi_di_qpmstator_yokeambienttorque
55789.47509019.04269457.689816-93.98364345.0504844999.954590-140.61593653.10114341.61501732.94459520.64077048.478104
55889.40403019.01984057.853970-93.93808745.1013954999.960449-140.65438853.10080741.65700532.94646820.56067548.560627
55989.54896519.03194257.958450-93.79789045.1361544999.952637-140.76254353.07302141.69025032.95995320.60089948.527035
56089.68190019.05550458.048870-93.68972845.1380504999.955566-140.89665253.06668541.71435232.97979720.70478148.581799
56189.85391219.08898258.078205-93.56863445.1542324999.959961-141.00155653.07550041.70050833.02584120.67936548.487282
56290.04657719.13820858.021954-93.44634245.1798524999.957031-141.01202453.07640841.72079833.06006220.57929048.483501
56390.18175519.17975458.029678-93.32598945.1965454999.955566-140.98237653.03592341.78850633.08887520.52134548.373051
56490.04560119.22655358.087345-93.39662945.2160494999.964355-141.01541153.04487641.85156233.10132620.36208548.419834
56590.09299519.25892858.053974-93.40736445.2329984999.951172-140.97868353.04801941.87928033.10936720.24797148.373577
56689.99385819.29486858.034531-93.44004845.2281464999.953125-141.02241553.03113641.90695633.11236620.16620348.357979